



'''simple docstring'''


import uuid
from typing import Any, Dict, List, Optional, Union

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
						import tensorflow as tf

if is_torch_available():
						import torch


lowercase_   :					List[str]        =     logging.get_logger(__name__)




class   __UpperCamelCase							:







	def __init__(     self			,					_lowerCAmelCase = None			,					_lowerCAmelCase = None			,					_lowerCAmelCase=None			,					_lowerCAmelCase=None    )     ->     Optional[int]:
							'''simple docstring'''






							if not conversation_id:
													lowercase   =				uuid.uuida()
							if past_user_inputs is None:
													lowercase   =				[]
							if generated_responses is None:
													lowercase   =				[]

							lowercase   =				conversation_id
							lowercase   =				past_user_inputs
							lowercase   =				generated_responses
							lowercase   =				text







	def __eq__(     self			,					_lowerCAmelCase    )     ->     Any:
							'''simple docstring'''






							if not isinstance(lowerCamelCase_			,					lowerCamelCase_    ):
													return False
							if self.uuid == other.uuid:
													return True
							return (
							    self.new_user_input == other.new_user_input
							    and self.past_user_inputs == other.past_user_inputs
							    and self.generated_responses == other.generated_responses
							)







	def 	_a (     self			,					_lowerCAmelCase			,					_lowerCAmelCase = False    )     ->     Union[str, Any]:
							'''simple docstring'''






							if self.new_user_input:
													if overwrite:
																			logger.warning(
																			    F"""User input added while unprocessed input was existing: \"{self.new_user_input}\" was overwritten """
																			    F"""with: \"{text}\"."""    )
																			lowercase   =				text
													else:
																			logger.warning(
																			    F"""User input added while unprocessed input was existing: \"{self.new_user_input}\" new input """
																			    F"""ignored: \"{text}\". Set `overwrite` to True to overwrite unprocessed user input"""    )
							else:
													lowercase   =				text







	def 	_a (     self    )     ->     Tuple:
							'''simple docstring'''






							if self.new_user_input:
													self.past_user_inputs.append(self.new_user_input    )
							lowercase   =				None







	def 	_a (     self			,					_lowerCAmelCase    )     ->     int:
							'''simple docstring'''






							self.generated_responses.append(lowerCamelCase_    )







	def 	_a (     self    )     ->     int:
							'''simple docstring'''






							for user_input, generated_response in zip(self.past_user_inputs			,					self.generated_responses    ):
													yield True, user_input
													yield False, generated_response
							if self.new_user_input:
													yield True, self.new_user_input







	def __repr__(     self    )     ->     Optional[int]:
							'''simple docstring'''






							lowercase   =				F"""Conversation id: {self.uuid} \n"""
							for is_user, text in self.iter_texts():
													lowercase   =				"""user""" if is_user else """bot"""
													output += F"""{name} >> {text} \n"""
							return output




@add_end_docstrings(
    __lowerCAmelCase      , R'''
        min_length_for_response (`int`, *optional*, defaults to 32):
            The minimum length (in number of tokens) for a response.
        minimum_tokens (`int`, *optional*, defaults to 10):
            The minimum length of tokens to leave for a response.
    '''      , )
class   __UpperCamelCase							(__lowerCAmelCase      ):







	def __init__(     self			,					*_lowerCAmelCase			,					**_lowerCAmelCase    )     ->     Any:
							'''simple docstring'''






							super().__init__(*lowerCamelCase_			,					**lowerCamelCase_    )
							if self.tokenizer.pad_token_id is None:
													lowercase   =				self.tokenizer.eos_token







	def 	_a (     self			,					_lowerCAmelCase=None			,					_lowerCAmelCase=None			,					_lowerCAmelCase=None			,					**_lowerCAmelCase    )     ->     str:
							'''simple docstring'''






							lowercase   =				{}
							lowercase   =				{}
							lowercase   =				{}

							if min_length_for_response is not None:
													lowercase   =				min_length_for_response
							if minimum_tokens is not None:
													lowercase   =				minimum_tokens

							if "max_length" in generate_kwargs:
													lowercase   =				generate_kwargs["""max_length"""]
													# self.max_length = generate_kwargs.get("max_length", self.model.config.max_length)
							if clean_up_tokenization_spaces is not None:
													lowercase   =				clean_up_tokenization_spaces

							if generate_kwargs:
													forward_params.update(lowerCamelCase_    )
							return preprocess_params, forward_params, postprocess_params







	def __call__(     self			,					_lowerCAmelCase			,					_lowerCAmelCase=0			,					**_lowerCAmelCase    )     ->     Dict:
							'''simple docstring'''






							lowercase   =				super().__call__(lowerCamelCase_			,					num_workers=lowerCamelCase_			,					**lowerCamelCase_    )
							if isinstance(lowerCamelCase_			,					lowerCamelCase_    ) and len(lowerCamelCase_    ) == 1:
													return outputs[0]
							return outputs







	def 	_a (     self			,					_lowerCAmelCase			,					_lowerCAmelCase=32    )     ->     Union[str, Any]:
							'''simple docstring'''






							if not isinstance(lowerCamelCase_			,					lowerCamelCase_    ):
													raise ValueError("""ConversationalPipeline, expects Conversation as inputs"""    )
							if conversation.new_user_input is None:
													raise ValueError(
													    F"""Conversation with UUID {type(conversation.uuid    )} does not contain new user input to process. """
													    """Add user inputs with the conversation's `add_user_input` method"""    )
							if hasattr(self.tokenizer			,					"""_build_conversation_input_ids"""    ):
													lowercase   =				self.tokenizer._build_conversation_input_ids(lowerCamelCase_    )
							else:
													# If the tokenizer cannot handle conversations, we default to only the old version
													lowercase   =				self._legacy_parse_and_tokenize(lowerCamelCase_    )

							if self.framework == "pt":
													lowercase   =				torch.LongTensor([input_ids]    )
							elif self.framework == "tf":
													lowercase   =				tf.constant([input_ids]    )
							return {"input_ids": input_ids, "conversation": conversation}







	def 	_a (     self			,					_lowerCAmelCase			,					_lowerCAmelCase=10			,					**_lowerCAmelCase    )     ->     Optional[Any]:
							'''simple docstring'''






							lowercase   =				generate_kwargs.get("""max_length"""			,					self.model.config.max_length    )

							lowercase   =				model_inputs["""input_ids"""].shape[1]
							if max_length - minimum_tokens < n:
													logger.warning(F"""Conversation input is to long ({n}), trimming it to ({max_length} - {minimum_tokens})"""    )
													lowercase   =				max_length - minimum_tokens
													lowercase   =				model_inputs["""input_ids"""][:, -trim:]
													if "attention_mask" in model_inputs:
																			lowercase   =				model_inputs["""attention_mask"""][:, -trim:]
							lowercase   =				model_inputs.pop("""conversation"""    )
							lowercase   =				max_length
							lowercase   =				self.model.generate(**lowerCamelCase_			,					**lowerCamelCase_    )
							if self.model.config.is_encoder_decoder:
													lowercase   =				1
							else:
													lowercase   =				n
							return {"output_ids": output_ids[:, start_position:], "conversation": conversation}







	def 	_a (     self			,					_lowerCAmelCase			,					_lowerCAmelCase=True    )     ->     Any:
							'''simple docstring'''






							lowercase   =				model_outputs["""output_ids"""]
							lowercase   =				self.tokenizer.decode(
							    output_ids[0]			,					skip_special_tokens=lowerCamelCase_			,					clean_up_tokenization_spaces=lowerCamelCase_			,					)
							lowercase   =				model_outputs["""conversation"""]
							conversation.mark_processed()
							conversation.append_response(lowerCamelCase_    )
							return conversation







	def 	_a (     self			,					_lowerCAmelCase    )     ->     Dict:
							'''simple docstring'''






							lowercase   =				self.tokenizer.eos_token_id
							lowercase   =				[]
							for is_user, text in conversation.iter_texts():
													if eos_token_id is not None:
																			input_ids.extend(self.tokenizer.encode(lowerCamelCase_			,					add_special_tokens=lowerCamelCase_    ) + [eos_token_id]    )
													else:
																			input_ids.extend(self.tokenizer.encode(lowerCamelCase_			,					add_special_tokens=lowerCamelCase_    )    )

							if len(lowerCamelCase_    ) > self.tokenizer.model_max_length:
													lowercase   =				input_ids[-self.tokenizer.model_max_length :]
							return input_ids



from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE 		=      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE 		=      {
    """sail/poolformer_s12""": """https://huggingface.co/sail/poolformer_s12/resolve/main/config.json""",
    # See all PoolFormer models at https://huggingface.co/models?filter=poolformer
}
class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase		):
   __lowerCAmelCase           =				"""poolformer"""







   def __init__(     self						:						str   ,   lowerCamelCase_						:						Any=3   ,   lowerCamelCase_						:						Any=16   ,   lowerCamelCase_						:						Any=16   ,   lowerCamelCase_						:						Optional[Any]=3   ,   lowerCamelCase_						:						List[str]=4.0   ,   lowerCamelCase_						:						Tuple=[2, 2, 6, 2]   ,   lowerCamelCase_						:						Optional[Any]=[64, 128, 320, 512]   ,   lowerCamelCase_						:						Tuple=[7, 3, 3, 3]   ,   lowerCamelCase_						:						Optional[Any]=[4, 2, 2, 2]   ,   lowerCamelCase_						:						Dict=[2, 1, 1, 1]   ,   lowerCamelCase_						:						Tuple=4   ,   lowerCamelCase_						:						Optional[int]=0.0   ,   lowerCamelCase_						:						Union[str, Any]="gelu"   ,   lowerCamelCase_						:						Union[str, Any]=True   ,   lowerCamelCase_						:						Union[str, Any]=1E-5   ,   lowerCamelCase_						:						int=0.0_2   ,   **lowerCamelCase_						:						str   ,   ):





    """simple docstring"""


    UpperCamelCase			  =      num_channels
    UpperCamelCase			  =      patch_size
    UpperCamelCase			  =      stride
    UpperCamelCase			  =      padding
    UpperCamelCase			  =      pool_size
    UpperCamelCase			  =      hidden_sizes
    UpperCamelCase			  =      mlp_ratio
    UpperCamelCase			  =      depths
    UpperCamelCase			  =      patch_sizes
    UpperCamelCase			  =      strides
    UpperCamelCase			  =      num_encoder_blocks
    UpperCamelCase			  =      drop_path_rate
    UpperCamelCase			  =      hidden_act
    UpperCamelCase			  =      use_layer_scale
    UpperCamelCase			  =      layer_scale_init_value
    UpperCamelCase			  =      initializer_range
    super().__init__(**lowerCamelCase_				)





class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase		):
   __lowerCAmelCase           =				version.parse("""1.11"""		)







   @property
   def 						lowerCamelCase_	(     self						:						Any				):





    """simple docstring"""


    return OrderedDict(
        [
            ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
        ]				)







   @property
   def 						lowerCamelCase_	(     self						:						Any				):





    """simple docstring"""


    return 2E-3
